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Significant advancements have been made in the
analysis of medical images through systems developed using deep
learning methods. The detection of fractures using computer
vision is one of the current areas under investigation. Identifying
bone fractures in X-ray images is a time-consuming process that
requires specialized expertise. Pediatric bone fractures, in
particular, are common and situations that require prompt
treatment to prevent future complications. Consequently, deep
learning-based detection systems hold great importance in
supporting clinical decision-making processes and saving time for
specialists. This study evaluates five recent YOLO algorithm
versions (YOLOv8-YOLOv12) for pediatric wrist fracture
detection in X-rays. The Pediatric Wrist Trauma X-ray dataset
(GRAZPEDWRI-DX) was used, and training was conducted on
10,300 X-ray images. The models' accuracy, speed, and
generalization capabilities were analyzed, and it was observed
that the YOLOv9s and YOLOv12m models achieved the best
performance (0.944 mAP50 and 0.90 Recall), with all trained
models showing similar results. This study aims to demonstrate
the performance of YOLO-based models in the automatic
detection of bone fractures in X-ray images, contributing to the
acceleration of the diagnostic process for these common injuries.
International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES
Ayşe Aybilge MURAT
Mustafa Servet KIRAN